Noisy Data and Label Noise
Explore the concept of noisy data and label noise in machine learning. Understand different sources of noise, reasons for mislabeling, and types of mislabeling, including unbiased and biased. This lesson helps you recognize how noise affects model accuracy and prepares you to manage label errors effectively.
What is noise?
Noise is defined as an undesirable behavior within data. Additionally, any data that a machine cannot easily understand or correctly interpret is also considered noise. In a dataset, noise can take various forms, including outliers, measurement errors, missing values, and labeling errors. It can distort the statistical properties of the data, introduce inaccuracies, and affect the analysis or training of ML models.
Unreliable data collection tools are a common source of errors in datasets, and these errors can be categorized as noise. Such errors arise from unreliable equipment and can substantially impact the accuracy of ML models.
We cannot eliminate noise while collecting and processing data, but we can minimize the chances of error through data cleansing and transformation.
Noise sources
The three main causes of noise are as follows:
Implicit errors: This type of error is caused by the inappropriate measurement of tools, potentially ...